Problems in the pathological classification of the common malignant gliomas complicate predicting patient prognosis and response to therapy. The long-term goal of this research is to provide a comprehensive understanding of the genetic events that characterize glioma tumorigenesis and to introduce genetic analyses into routine classification. During the two prior funding periods of this grant, we have clarified genetic events that underlie glioma formation, have correlated genotype with clinicopathological parameters, and have introduced genetic analyses into clinical practice. This work now raises three hypotheses that can be tested using proven translational research strategies. 1) To test the hypothesis that molecular profiles can divide histologically classic and non-classic malignant gliomas into improved prognostic and predictive subgroups in a practical manner, we propose to define markers capable of robust distinction of chemosensitive and better prognosis malignant gliomas from chemoresistant and poor prognosis malignant gliomas. 2) To test the hypothesis that increased cellular invasion correlates with prognosis independent of histological appearance and that such a molecular phenotype can be readily detected, we propose to define an "invasion genotype/phenotype" that correlates with clinicopathological endpoints in patients with malignant gliomas. 3) Finally, to test the hypothesis that ongoing selection pressures operate to determine tumor genotype, and that molecular diagnostic approaches should take such heterogeneity into account, we propose to characterize intratumoral selection in glioblastoma perinecrotic pseudopalisades to define a molecular signature that correlates with clinicopathological endpoints in patients with anaplastic astrocytomas. Each aim thus tests a related hypothesis that a particular molecular analysis can augment current approaches to glioma classification, and each aim also follows a similar experimental design. The further clarification of the genetic basis of human gliomas will continue to contribute to a glioma classification system that will more accurately reflect tumor behavior and response to therapy than current histopathological schemes.